Tootfinder

Semantically Reflected Programs
Eduard Kamburjan, Vidar Norstein Klungre, Yuanwei Qu, Rudolf Schlatte, Egor V. Kostylev, Martin Giese, Einar Broch Johnsen
https://arxiv.org/abs/2509.03318

Semantically Reflected Programs
This paper addresses the dichotomy between the formalization of structural and the formalization of behavioral knowledge by means of semantically lifted programs, which explore an intuitive connection between programs and knowledge graphs. While knowledge graphs and ontologies are eminently useful to represent formal knowledge about a system's individuals and universals, programming languages are designed to describe the system's evolution. To address this dichotomy, we introduce a semantic lif…

This https://arxiv.org/abs/2506.00202 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

What do professional software developers need to know to succeed in an age of Artificial Intelligence?
Generative AI is showing early evidence of productivity gains for software developers, but concerns persist regarding workforce disruption and deskilling. We describe our research with 21 developers at the cutting edge of using AI, summarizing 12 of their work goals we uncovered, together with 75 associated tasks and the skills & knowledge for each, illustrating how developers use AI at work. From all of these, we distilled our findings in the form of 5 insights. We found that the skills & know…

NoteBar: An AI-Assisted Note-Taking System for Personal Knowledge Management
Josh Wisoff, Yao Tang, Zhengyu Fang, Jordan Guzman, YuTang Wang, Alex Yu
https://arxiv.org/abs/2509.03610

NoteBar: An AI-Assisted Note-Taking System for Personal Knowledge Management
Note-taking is a critical practice for capturing, organizing, and reflecting on information in both academic and professional settings. The recent success of large language models has accelerated the development of AI-assisted tools, yet existing solutions often struggle with efficiency. We present NoteBar, an AI-assisted note-taking tool that leverages persona information and efficient language models to automatically organize notes into multiple categories and better support user workflows. T…

This https://arxiv.org/abs/2404.11841 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLO_…

On the Unprovability of Circuit Size Bounds in Intuitionistic $\mathsf{S}^1_2$
We show that there is a constant $k$ such that Buss's intuitionistic theory $\mathsf{IS}^1_2$ does not prove that SAT requires co-nondeterministic circuits of size at least $n^k$. To our knowledge, this is the first unconditional unprovability result in bounded arithmetic in the context of worst-case fixed-polynomial size circuit lower bounds. We complement this result by showing that the upper bound $\mathsf{NP} \subseteq \mathsf{coNSIZE}[n^k]$ is unprovable in $\mathsf{IS}^1_2$. In order to…

Algorithmic Evaluation and the Marginalization of Single Authorship in Management Science
Wei Meng
https://arxiv.org/abs/2508.00842 https://arxiv.org/pdf/2…

Algorithmic Evaluation and the Marginalization of Single Authorship in Management Science
The decline of single authorship in peer-reviewed journals within the current collaboration-oriented knowledge production framework has prompted deeper reflection on the shifting power structures in academic systems. This paper aims to explore the underlying institutional logic and evaluation mechanisms contributing to the marginalization of single-author research in the management field. It further investigates how the discourse of collaborative advantage conceals structural power redistributi…

This https://arxiv.org/abs/2205.00368 has been replaced.
link: https://scholar.google.com/scholar?q=a

Direct closed-loop identification of continuous-time systems using fixed-pole observer model
This paper provides a method for obtaining a continuous-time model of a target system in closed-loop from input-output data alone, in the case where no knowledge of the controllers nor excitation signals is available and I/O data may suffer from unknown offsets. The proposed method is based on a fixed-pole observer model, which is a reasonable continuous-time version corresponding to the innovation model in discrete-time and allows the identification of unstable target systems. Furthermore, it …

This https://arxiv.org/abs/2411.16331 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMM_…

Sonic: Shifting Focus to Global Audio Perception in Portrait Animation
The study of talking face generation mainly explores the intricacies of synchronizing facial movements and crafting visually appealing, temporally-coherent animations. However, due to the limited exploration of global audio perception, current approaches predominantly employ auxiliary visual and spatial knowledge to stabilize the movements, which often results in the deterioration of the naturalness and temporal inconsistencies.Considering the essence of audio-driven animation, the audio signal…

The Living Library of Trees: Mapping Knowledge Ecology in the Arnold Arboretum
Johan Malmstedt, Giacomo Nanni, Dario Rodighiero
https://arxiv.org/abs/2509.00114 https://

The Living Library of Trees: Mapping Knowledge Ecology in the Arnold Arboretum
As biodiversity loss and climate change accelerate, botanical gardens serve as vital infrastructures for research, education, and conservation. This project focuses on the Arnold Arboretum of Harvard University, a 281-acre living museum founded in 1872 in Boston. Drawing on more than a century of curatorial data, the research combines historical analysis with computational methods to visualize the biographies of plants and people. The resulting platform reveals patterns of care and scientific o…

AMBEDKAR-A Multi-level Bias Elimination through a Decoding Approach with Knowledge Augmentation for Robust Constitutional Alignment of Language Models
Snehasis Mukhopadhyay, Aryan Kasat, Shivam Dubey, Rahul Karthikeyan, Dhruv Sood, Vinija Jain, Aman Chadha, Amitava Das
https://arxiv.org/abs/2509.02133

AMBEDKAR-A Multi-level Bias Elimination through a Decoding Approach with Knowledge Augmentation for Robust Constitutional Alignment of Language Models
Large Language Models (LLMs) can inadvertently reflect societal biases present in their training data, leading to harmful or prejudiced outputs. In the Indian context, our empirical evaluations across a suite of models reveal that biases around caste and religion are particularly salient. Yet, most existing mitigation strategies are Western-centric and fail to address these local nuances. We propose AMBEDKAR, a framework inspired by the egalitarian vision of Dr B. R. Ambedkar, architect of the …

This https://arxiv.org/abs/2505.23703 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Let's Reason Formally: Natural-Formal Hybrid Reasoning Enhances LLM's Math Capability
Enhancing the mathematical reasoning capabilities of LLMs has garnered significant attention in both the mathematical and computer science communities. Recent works have made substantial progress in both Natural Language (NL) reasoning and Formal Language (FL) reasoning by leveraging the potential of pure Reinforcement Learning (RL) methods on base models. However, RL approaches struggle to impart new capabilities not presented in the base model, highlighting the need to integrate more knowledg…